三个问题：关于人工智能与数理科学的未来

# 3 Questions: On the future of AI and the mathematical and physical sciences 来源：https://news.mit.edu/2026/3-questions-future-of-ai-and-mathematical-physical-sciences-0311 *好奇心驱动的研究长期以来一直在推动技术变革。一个世纪前，对原子的探索催生了量子力学，并最终孕育了现代计算机核心的晶体管。反之，蒸汽机是一项实用技术的突破，但直到热力学的基础研究出现后，人类才真正充分发挥了其潜力。* *如今，人工智能与科学同样站在了关键的转折点上。当前的AI革命得益于数学与物理科学（MPS）数十年的研究积累，这些研究提供了现代AI得以实现的难题、数据集和深刻洞见。2024年诺贝尔物理学奖和化学奖的颁发——分别表彰了植根于物理学的底层AI方法以及AI在蛋白质设计中的应用——让这一联系变得不容忽视。* *2025年，MIT主办了Workshop on the Future of AI+MPS (https://arxiv.org/abs/2509.02661)，该活动由NSF资助，并获得MIT理学院以及物理系、化学系和数学系的支持。研讨会汇聚了顶尖的AI与科学研究者，共同探讨MPS领域如何最好地把握机遇并利用AI塑造其未来，同时为AI的未来发展贡献力量。目前，一份面向资助机构、院校和研究人员的白皮书已在Machine Learning: Science and Technology (https://iopscience.iop.org/article/10.1088/2632-2153/ae3e4e)期刊上发布。在此专访中，MIT物理学教授兼研讨会主席Jesse Thaler阐述了核心主题，以及MIT如何定位自身以在AI与科学领域领跑。* **Q:** What are the report’s key themes regarding last year’s gathering of leaders across the mathematical and physical sciences? **A:** Gathering so many researchers at the forefront of AI and science in one room was illuminating. Though the workshop participants came from five distinct scientific communities — astronomy, chemistry, materials science, mathematics, and physics — we found many similarities in how we are each engaging with AI. A real consensus emerged from our animated discussions: Coordinated investment in computing and data infrastructures, cross-disciplinary research techniques, and rigorous training can meaningfully advance both AI and science. One of the central insights was that this has to be a two-way street. It’s not just about using AI to do better science; science can also make AI better. Scientists excel at distilling insights from complex systems, including neural networks, by uncovering underlying principles and emergent behaviors. We call this the “science of AI,” and it comes in three flavors: science driving AI, where scientific reasoning informs foundational AI approaches; science inspiring AI, where scientific challenges push the development of new algorithms; and science explaining AI, where scientific tools help illuminate how machine intelligence actually works. In my own field of particle physics, for instance, researchers are developing real-time AI algorithms to handle the data deluge from collider experiments. This work has direct implications for discovering new physics, but the algorithms themselves turn out to be valuable well beyond our field. The workshop made clear that the science of AI should be a community priority — it has the potential to transform how we understand, develop, and control AI systems. Of course, bridging science and AI requires people who can work across both worlds. Attendees consistently emphasized the need for “centaur scientists” — researchers with genuine interdisciplinary expertise. Supporting these polymaths at every career stage, from integrated undergraduate courses to interdisciplinary PhD programs to joint faculty hires, emerged as essential. **Q:** How do MIT’s AI and science efforts align with the workshop recommendations? **A:** The workshop framed its recommendations around three pillars: research, talent, and community. As director of theNSF Institute for Artificial Intelligence and Fundamental Interactions (https://iaifi.org/)(IAIFI) — a collaborative AI and physics effort among MIT and Harvard, Northeastern, and Tufts universities — I’ve seen firsthand how effective this framework can be. Scaling this up to MIT, we can see where progress is being made and where opportunities lie. On the research front, MIT is already enabling AI-and-science work in both directions. Even a quick scroll throughMIT Newsshow shows how individual researchers across the School of Science are pursuing AI-driven projects, building a pipeline of knowledge and surfacing new opportunities. At the same time, collaborative efforts like IAIFI and theAccelerated AI Algorithms for Data-Driven Discovery (A3D3) Institute (https://a3d3.ai/) concentrate interdisciplinary energy for greater impact. TheMIT Generative AI Impact Consortium (https://genai.mit.edu/)is also supporting application-driven AI work at the university scale. To foster early-career AI-and-science talent, several initiatives are training the next generation of centaur scientists. The MIT Schwarzman College of Computing'sCommon Ground for Computing Education program (https://computing.mit.edu/cross-cutting/common-ground-for-computing-education/) helps students become “bilingual” in computing and their home discipline. Interdisciplinary PhD pathways are also gaining traction; IAIFI worked with theMIT Institute for Data, Systems, and Society (https://idss.mit.edu/)to create one in physics, statistics, and data science, and about 10 percent of physics PhD students now opt for it — a number that's likely to grow. Dedicated postdoctoral roles like theIAIFI Fellowship (https://iaifi.org/current-fellows.html)andTayebati Fellowship (https://computing.mit.edu/research/postdoctoral-fellows-programs/tayebati-postdoctoral-fellowship-program/) give early-career researchers the freedom to pursue interdisciplinary work. Funding centaur scientists and giving them space to build connections across domains, universities, and career stages has been transformative. Finally, community-building ties it all together. From focused workshops to large symposia, organizing interdisciplinary events signals that AI and science isn’t siloed work — it’s an emerging field. MIT has the talent and resources to make a significant impact, and hosting these gatherings at multiple scales helps establish that leadership. **Q:** What lessons can MIT draw about further advancing its AI-and-science efforts? **A:** The workshop crystallized something important: The institutions that lead in AI and science will be the ones that think systematically, not piecemeal. Resources are finite, so priorities matter. Workshop attendees were clear about what becomes possible when an institution coordinates hires, research, and training around a cohesive strategy. MIT is well positioned to build on what’s already underway with more structural initiatives — joint faculty lines across computing and scientific domains, expanded interdisciplinary degree pathways, and deliberate “science of AI” funding. We’re already seeing moves in this direction; this year, the MIT Schwarzman College of Computing and the Department of Physics are conducting their first-ever joint faculty search, which is exciting to see. The virtuous cycle of AI and science has the potential to be truly transformative — offering deeper insight into AI, accelerating scientific discovery, and producing robust tools for both. By developing an intentional strategy, MIT will be well positioned to lead in, and benefit from, the coming waves of AI.